Computing and Information Systems - Theses
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ItemService value in business-to-business cloud computing( 2014)This thesis is concerned with determining and measuring the components of service value in the business-to-business cloud computing context. Although service value measurement and its perceptions have been identified as key issues for researchers and practitioners, theoretical and empirical studies have experienced great challenges in measuring perceptions of service value in numerous business contexts. The thesis first determines the components of service value and then measures the service value perceptions of users in a business-to-business context of cloud computing. In this thesis, I: • undertook qualitative in-depth interviews (N=21) of managers who are responsible for deciding on the adoption and maintenance of cloud computing services. Two key findings of the interviews are that the four components of an established service value model in a business-to-consumer setting are appropriate in a business-to-business context of cloud computing and found evidence that an additional component, which we call cloud service governance, applies and does not fit the existing four components; • conducted a survey (N=328) of cloud computing practitioners to demonstrate that the findings from the qualitative in-depth interviews are generalisable to a number of industry sectors and across geographical locations; • assessed the measurement models, comprising both reflective and formative, and structural model by using partial least squares structural equation modeling, and provided evidence of specifying Service Value as a formative second-order hierarchical latent variable by using a sequential latent variable score method; • demonstrated that Service Equity is not a statistically significant component of service value in the first-order model, Service Quality is consistently significant for both first-order model and second-order, formative model, and the additional construct called Cloud Service Governance is significant; and, • for the first time, fully tested a reliable service value instrument for use by the customers of cloud computing, and aiming to engage cloud service providers in order to enhance customer satisfaction and increase repurchase intentions.
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ItemResource provisioning in spot market-based cloud computing environments( 2014)Recently, cloud computing providers have started offering unused computational resources in the form of dynamically priced virtual machines (VMs), also known as "spot instances". In spite of the apparent economical advantage, an intermittent nature is inherent to these biddable resources, which may cause VM unavailability. When an out-of-bid situation occurs, i.e. the current spot price goes above the user's maximum bid, spot instances are terminated by the provider without prior notice. This thesis presents a study on employing cloud computing spot instances as a means of executing computational jobs on cloud computing resources. We start by proposing a resource management and job scheduling policy, named SpotRMS, which addresses the problem of running deadline-constrained compute-intensive jobs on a pool of low-cost spot instances, while also exploiting variations in price and performance to run applications in a fast and economical way. This policy relies on job runtime estimations to decide what are the best types of spot instances to run each job and when jobs should run. It is able to minimise monetary spending and make sure jobs finish within their deadlines. We also propose an improvement for SpotRMS, that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault tolerance techniques, namely checkpointing, task duplication and migration. As a further improvement, we equip SpotRMS with prediction-assisted resource provisioning and bidding strategies. Our results demonstrate that both costs savings and strict adherence to deadlines can be achieved when properly combining and tuning the policy mechanisms. Especially, the fault tolerance mechanism that employs migration of VM state provides superior results in virtually all metrics. Finally, we employ a statistical model of spot price dynamics to artificially generate price patterns of varying volatility. We then analyse how SpotRMS performs in environments with highly variable price levels and more frequent changes. Fault tolerance is shown to be even more crucial in such scenarios.